The Energy Efficiency Gap and Energy Price Responsiveness in Food Processing
June 2020
Working Paper Number:
CES-20-18
Abstract
This paper estimates stochastic frontier energy demand functions with non-public, plant-level data from the U.S. Census Bureau to measure the energy efficiency gap and energy price elasticities in the food processing industry. The estimates are for electricity and fuel use in 4 food processing sectors, based on the disaggregation of this industry used by the National Energy Modeling System Industrial Demand Module. The estimated demand functions control for plant inputs and output, energy prices, and other observables including 6-digit NAICS industry designations. Own price elasticities range from 0.6 to -0.9 with little evidence of fuel/electricity substitution. The magnitude of the efficiency estimates is sensitive to the assumptions but consistently reveal that few plants achieve 100% efficiency. Defining a 'practical level of energy efficiency' as the 95th percentile of the efficiency distributions and averaging across all the models result in a ~20% efficiency gap. However, most of the potential reductions in energy use from closing this efficiency gap are from plants that are 'low hanging fruit'; 13% of the 20% potential reduction in the efficiency gap can be obtained by bringing the lower half of the efficiency distribution up to just the median level of observed performance. New plants do exhibit higher energy efficiency than existing plants which is statistically significant, but the difference is small for most of the industry; ranging from a low of 0.4% to a high of 5.7%.
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econometrically,
demand,
econometric,
estimating,
cost,
efficiency,
efficient,
expenditure,
fuel,
agricultural,
consumption,
electricity,
energy prices,
energy,
energy efficiency,
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Energy Information Administration,
Organization for Economic Cooperation and Development,
Manufacturing Energy Consumption Survey,
Chicago Census Research Data Center,
Census of Manufacturing Firms,
Research Data Center,
North American Industry Classification System,
Special Sworn Status,
State Energy Data System,
Duke University,
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Social Science Research Institute,
Federal Statistical Research Data Center
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